Cleveland’s Changing Population

Exploring the US Census with R

Published

January 30, 2025

The resurgence of people moving to downtown Cleveland is making news.¹ According to a study commissioned by Downtown Cleveland Inc., the downtown population was almost 19,000 in the 2020 census, a 22% increase from 2010.² However, Cleveland Open Data shows only 13,000³. Cleveland Scene reports that there are lots of estimates out there, one as low as 8,000!⁴ What gives? The organizations may be using different sources, like the decennial US census vs the more recent, but less comprehensive, American Community Survey. But it seems more likely they are using different geographic boundaries.

I was able to reproduce some estimates. My main tools to do this were the tidycensus R package for US Census data, and the Cleveland Open Data service for Cleveland neighborhood definitions. I’ll step through the process below.

Note

This is a work file / tutorial. Researching Cleveland’s population is mostly a toy project to experiment with R tools that work with APIs. This should come in handy for some future project. If you are not me, I hope this helps with whatever you’re doing. Otherwise, ‘hello, future me!’ You can find the source code and downloaded data on my GitHub page.

Defining “Downtown”

Cleveland extends from Cleveland Hopkins Airport on the west all the way to Euclid on the east. It’s mostly bounded on the south by I-80. Here is the map from the Cleveland Wikipedia Page.

Screen capture from Cleveland article on Wikipedia.

The 2020 US decennial census counted 372K people in Cleveland.⁵ That’s a decline from 397K in 2010. The 1-year American Community Survey (ACS) shows it is still falling, down to 363K in 2023.⁶ But the decline is uneven, and parts of the city are actually growing, including the downtown area. There is no official definition of downtown, so we can make some choices. The Census Bureau provides the building blocks for a definition: over 15K census blocks in Cleveland, rolled up to around 200 census tracts.

Cleveland’s City Planning Commission (CPC) defines 34 neighborhoods for urban planning initiatives.⁷ They are commonly referred to Statistical (or Social) Planning Areas (SPAs). I pasted a pdf map from the CPC below. You can see there is an SPA actually named “Downtown”. It’s bounded by the Cuyahoga River and I-90. Cleveland Open Data has an interactive map that you can explore and download. I downloaded and extracted its shapefile to my local drive.

Screen capture from City Planning Commission 2010 Census pdf.

So that is one definition. A second one comes from a study by Urban Partners that was commission by Downtown Cleveland, Inc. in 2023. Page 3 of the pdf report (copy/pasted below) shows a Westside and a Downtown Core. Whereas the Downtown SPA had about 13.3K people in the 2020 census, this Downtown Core had 18.7K people. The main differences are that Urban Partners took a bite out of the Central neighborhood on the east side, and parts of the West Bank of the Flats in the Cuyahoga Valley and Ohio City neighborhoods on the west side.

Downtown Cleveland Market Study Report, p3. Urban Partners.

Blocks, Tracts, and Subdivisions

Let’s gather the materials to segment population estimates into these boundaries. Several R libraries make it easy to work with census data. The tidycencus package was developed to interface with the US Census Bureau APIs. It also returns feature geometries for spatial analysis. The tigris package works with the Census Bureau’s TIGER/Line shape files, and the sf (simple features) package performs spatial operations.

library(tidyverse)
library(glue)
library(scales)
library(gt)
library(ggiraph)  # interactive plots

library(tidycensus)
library(tigris) # TIGER/Line shapefiles
library(sf)  # simple features for spatial analysis

Let’s get the CPC’s definition of neighborhoods. I went to the City of Cleveland Open Data web site and and navigated to their analysis of the 2020 US Census.⁸ There interactive map has five layers (screen capture below). The first is the shape file of the 34 neighborhoods (SPAs). The second file contains population data from the 2020 decennial census complete with census block, census tract, and SPA. I downloaded and unzipped the first two files. Now I have a way to map the SPA boundaries within Cleveland, and I have a mapping of census blocks to SPAs so I can join this to the US Census data.

# Nice contiguous shape file. One record for each of the 34 SPAs.
cleve_neigh_0 <-
  st_read(file.path(
  "inputs/Cleveland Neighborhoods",
  "Neighborhood_Population_Change.shp"
))

# Cleveland populated blocks. Includes block, tract, and SPA name.
cleve_blocks <- st_read(file.path(
  "inputs/Cleveland Populated Blocks 2020",
  "Decennial_2020_Populated_Blocks_Cleveland_Only.shp"
)) |>
  select(-starts_with("P0"), -starts_with("H0"))

I could just join cleve_neigh_0 to US Census Bureau data files by the geography elements using the sf package. I know exactly which blocks belong in each SPA for 2020, but block definitions change across censuses, so joining to cleve_neigh_0 will get me the 2000 and 2010 figures. The shape file may not be perfectly precise because I can’t quite match quoted population estimates for 2000 and 2010, but it’s close.

Load shape files from the tigris package to facilitate mapping. I’ll get the state, county boundaries, and a few cities. I also got the Terminal town coordinates from Google.

oh_state <- tigris::states(cb = TRUE) |> filter(STUSPS == "OH") 

oh_counties <- tigris::counties(cb = TRUE) |> filter(STUSPS == "OH")
cuya_county <- oh_counties |> filter(NAME == "Cuyahoga")

oh_places <- tigris::places("OH", year = 2022)
x <- c("Cleveland Heights", "Mayfield Heights")
my_places <- oh_places |> filter(NAME %in% x) |> st_centroid()

terminal_tower <- st_sfc(st_point(c(-81.69387, 41.49824)), crs = 4326)

Here is a plot of Cuyahoga County, Cleveland, and its 34 SPAs. Hover over the shapes to see their names. There’s Terminal Tower in the heart of downtown. Progressive field is a few blocks away, and 7.0 miles from my home in Cleveland Heights. Mayfield is where Progressive Insurance is headquartered. That’s where I work when I need to go into the office (I work from home).

Show the code

p <-
  ggplot() +
  geom_sf(data = oh_state, color = "gray60") +
  geom_sf_interactive(
    data = oh_counties, 
    aes(tooltip = NAME),
    fill = "honeydew", color = "gray90"
  ) +
  geom_sf(data = cuya_county, fill = "honeydew2", color = "gray80") +
  geom_sf_interactive(
    data = cleve_neigh_0,
    aes(tooltip = SPA_NAME),
    fill = "honeydew3", color = "honeydew4"
  ) +
  geom_sf(data = my_places, color = "honeydew3") +
  geom_sf(data = terminal_tower, color = "firebrick") +
  geom_sf_text(data = terminal_tower, aes(label = "Terminal Tower"),
               size = 3, hjust = .2, vjust = 1, color = "firebrick") +
  geom_sf_text(data = my_places, aes(label = NAME), size = 3, hjust = .2, vjust = 1) +
  coord_sf(xlim = c(-82.0, -81.3), ylim = c(41.25, 41.65)) +
  theme(
    panel.background = element_rect(fill = "skyblue"),
    panel.grid = element_blank(),
    axis.text = element_blank()
  ) +
  labs(
    x = NULL, y = NULL, 
    title = glue("Cleveland and Surrounding Cities, Cuyahoga County")
  )

girafe(ggobj = p)

Census Data

I don’t want to abuse the US Census Bureau API, so I’ll set a flag to only download data as I’m developing this script. Once I have what I want, I’ll keep my data on my local drive and build my report.

USE_API <- FALSE

The Census Bureau API allows you to select multiple variables from a single census file. There are a few files for each census, and the variable names change. I want the Cleveland area population in 2000, 2010, 2020, and the American Community Survey (ACS) 1-year estimate from 2023 (most recent). So despite the handiness of tidycensus package, data collection is still going to be a bit tedious.

The decennial census developer page lists the accessible datasets: 2000, 2010, and 2020. You need an API key from the Bureau before you can do anything. This is quick and easy: just click the “Request a KEY” tile in the menu at the left. The Census Bureau emails you a key. Best practice is to save the key in an .Renviron file.

usethis::edit_r_environ(scope = "project")

This opens (or creates) a .Renviron file in your project root. Add your key. The name is important: CENSUS_API_KEY. The tidycensus functions send that system variable (if you don’t explicitly supply it in the function). Set it like this:

CENSUS_API_KEY="abc123"

Now you can pull census data. I’ll start with 2020.

2020

The decennial census developer page has several data files for each census. Through trial and error, I discovered Redistricting Data (PL 94-171) contains overall population. There is a full list of variables that represent the various sub-groups of the population. I used it and the tidycensus::load_variables() function to identify the ones I want. I’ll include race/ethnicity to investigate demographic trends.

pl_2020_vars <-
  tidycensus::load_variables(2020, "pl") |>
  filter(
    between(name, "P2_001N", "P2_011N"),
    !name %in% c("P2_003N", "P2_004N")
  )

Here they are after a bit of cleaning.

Show the code

pl_2020_vars <- 
  pl_2020_vars |>
  mutate(
    label = case_when(
      str_detect(label, "White") ~ "White",
      str_detect(label, "Black") ~ "Black",
      str_detect(label, "Asian") ~ "Asian",
      str_detect(label, "American Indian") ~ "American Indian",
      str_detect(label, "Native Hawaiian") ~ "Pacific Islander",
      str_detect(label, "Some Other Race") ~ "Other",
      str_detect(label, "two or more races") ~ "Two or more races",
      str_detect(label, "Hispanic") ~ "Hispanic",
      str_detect(label, "Total") ~ "Total",
      TRUE ~ label
    ),
    rpt_group = if_else(name == "P2_001N", "Total", "Race/ethnicity"),
    rpt_level = if_else(
      label %in% c("White", "Black", "Hispanic", "Asian", "Total"),
      label, "Other")
  ) |>
  select(variable = name, label, rpt_group, rpt_level)

pl_2020_vars

# A tibble: 9 × 4
  variable label             rpt_group      rpt_level
  <chr>    <chr>             <chr>          <chr>    
1 P2_001N  Total             Total          Total    
2 P2_002N  Hispanic          Race/ethnicity Hispanic 
3 P2_005N  White             Race/ethnicity White    
4 P2_006N  Black             Race/ethnicity Black    
5 P2_007N  American Indian   Race/ethnicity Other    
6 P2_008N  Asian             Race/ethnicity Asian    
7 P2_009N  Pacific Islander  Race/ethnicity Other    
8 P2_010N  Other             Race/ethnicity Other    
9 P2_011N  Two or more races Race/ethnicity Other

The Demographic Profile contains age data.

dp_2020_vars <- 
  tidycensus::load_variables(2020, "dp") |>
  filter(
    str_detect(label, "Count!!SEX AND AGE!!Total population"),
    !str_detect(label, "Selected Age Categories"),
    name != "DP1_0001C"
  )

I’ll ignore sex and aggregate the ages into ten-year buckets.

Show the code

dp_2020_vars <- 
  dp_2020_vars |>
  mutate(
    label = str_remove_all(label, "(Count!!SEX AND AGE!!Total population)|(!!)"),
    label = if_else(label == "", "Total", label),
    rpt_group = "Age",
    rpt_level = case_when(
      name <= "DP1_0004C" ~ "Under 15 yrs",
      name <= "DP1_0006C" ~ "15 to 24 yrs",
      name <= "DP1_0008C" ~ "25 to 34 yrs",
      name <= "DP1_0010C" ~ "35 to 44 yrs",
      name <= "DP1_0012C" ~ "45 to 54 yrs",
      name <= "DP1_0014C" ~ "55 to 64 yrs",
      TRUE ~ "65+ yrs"
    )
  ) |>
  select(variable = name, label, rpt_group, rpt_level)

dp_2020_vars

# A tibble: 18 × 4
   variable  label             rpt_group rpt_level   
   <chr>     <chr>             <chr>     <chr>       
 1 DP1_0002C Under 5 years     Age       Under 15 yrs
 2 DP1_0003C 5 to 9 years      Age       Under 15 yrs
 3 DP1_0004C 10 to 14 years    Age       Under 15 yrs
 4 DP1_0005C 15 to 19 years    Age       15 to 24 yrs
 5 DP1_0006C 20 to 24 years    Age       15 to 24 yrs
 6 DP1_0007C 25 to 29 years    Age       25 to 34 yrs
 7 DP1_0008C 30 to 34 years    Age       25 to 34 yrs
 8 DP1_0009C 35 to 39 years    Age       35 to 44 yrs
 9 DP1_0010C 40 to 44 years    Age       35 to 44 yrs
10 DP1_0011C 45 to 49 years    Age       45 to 54 yrs
11 DP1_0012C 50 to 54 years    Age       45 to 54 yrs
12 DP1_0013C 55 to 59 years    Age       55 to 64 yrs
13 DP1_0014C 60 to 64 years    Age       55 to 64 yrs
14 DP1_0015C 65 to 69 years    Age       65+ yrs     
15 DP1_0016C 70 to 74 years    Age       65+ yrs     
16 DP1_0017C 75 to 79 years    Age       65+ yrs     
17 DP1_0018C 80 to 84 years    Age       65+ yrs     
18 DP1_0019C 85 years and over Age       65+ yrs

With the variables identified, request the data from the API. Cleveland is one of 59 subdivisions within Cuyahoga County. I’ll download the subdivision data to get a total count for Cuyahoga County and for Cleveland. Counties are composed of census tracts, and census tracts are composed of census blocks. Cities overlap census tracts, so I’ll download the block-level data and join to the cleve_blocks dataset from Cleveland Open Data. Urban Partners defined their Downtown Core and Westside areas by tract and some blocks from the Central SPA. I figured out which tracts and blocks by studying their map and swearing a lot.

Show the code

# Utility function to create factors
my_rpt_relevel <- function(x) {
  ethn <- c("Black", "White", "Hispanic", "Asian", "Other", "Total")
  x <- fct_relevel(x, ethn, after = Inf)
  x <- fct_relevel(x, "Under 15 yrs", after = 0)
  return(x)
}

# Urban Partners defn of Westside, uses tracts. The block-level data
# includes the tract number.
westside_tracts_2020 <- c(
  "103100", "103400", "103500", "103602", "103800", "103900", "104100", 
  "104200", "104300", "197800", "197700", "197500", "104400"
)

# Urban Partners defn of Downtown Core, uses tracts and blocks
downtown_core_tracts_2020 <- c(
  "103300", "107101", "107701", "107802", "109301")
downtown_core_blocks_2020 <- paste0(
  "39035108701", c("2001", "2004", "2006", "2008"))

if (USE_API) {

  subdiv_2020_pl <- 
    get_decennial( 
      geography = "county subdivision",
      sumfile = "pl",
      variables = pl_2020_vars$variable,
      state = "OH",
      county = "Cuyahoga",
      geometry = TRUE, 
      year = 2020
    ) |> 
    inner_join(pl_2020_vars, by = "variable") |>
    summarize(
      .by = c(GEOID, NAME, geometry, rpt_group, rpt_level),
      value = sum(value)
    )
  
  subdiv_2020_dp <- 
    get_decennial( 
      geography = "county subdivision",
      sumfile = "dp",
      variables = dp_2020_vars$variable,
      state = "OH",
      county = "Cuyahoga",
      geometry = TRUE, 
      year = 2020
    ) |> 
    inner_join(dp_2020_vars, by = "variable") |>
    summarize(
      .by = c(GEOID, NAME, geometry, rpt_group, rpt_level),
      value = sum(value)
    )
  
  # tract_2020_pl <- 
  #   get_decennial( 
  #     geography = "tract",
  #     sumfile = "pl",
  #     variables = pl_2020_vars$variable,
  #     state = "OH",
  #     county = "Cuyahoga",
  #     geometry = TRUE, 
  #     year = 2020
  #   ) |> 
  #   inner_join(pl_2020_vars, by = "variable") |>
  #   summarize(
  #     .by = c(GEOID, NAME, geometry, rpt_group, rpt_level),
  #     value = sum(value)
  #   )
  # 
  # tract_2020_dp <- 
  #   get_decennial( 
  #     geography = "tract",
  #     sumfile = "dp",
  #     variables = dp_2020_vars$variable,
  #     state = "OH",
  #     county = "Cuyahoga",
  #     geometry = TRUE, 
  #     year = 2020
  #   ) |>
  #   inner_join(dp_2020_vars, by = "variable") |>
  #   summarize(
  #     .by = c(GEOID, NAME, geometry, rpt_group, rpt_level),
  #     value = sum(value)
  #   )
  
  block_2020_pl <- 
    get_decennial( 
      geography = "block",
      sumfile = "pl",
      variables = pl_2020_vars$variable,
      state = "OH",
      county = "Cuyahoga",
      geometry = TRUE, 
      year = 2020
    ) |> 
    inner_join(pl_2020_vars, by = "variable") |>
    summarize(
      .by = c(GEOID, NAME, geometry, rpt_group, rpt_level),
      value = sum(value)
    )
  
  # dp is not available at the block level

  subdiv_2020 <- 
    bind_rows(subdiv_2020_pl, subdiv_2020_dp) |>
    mutate(
      rpt_level = my_rpt_relevel(rpt_level),
      NAME = str_remove_all(NAME, "(, Cuyahoga County, Ohio)|(village)|(city)"),
      NAME = str_trim(NAME)
    )
           
  tract_2020 <-
    bind_rows(tract_2020_pl, tract_2020_dp) |>
    mutate(rpt_level = my_rpt_relevel(rpt_level))
  
  block_2020 <-
    block_2020_pl |>
    inner_join(
      cleve_blocks |> as_tibble() |> select(GEOID20, SPA = SPA_NAME), 
      by = c("GEOID" = "GEOID20")
    ) |>
    mutate(
      rpt_level = my_rpt_relevel(rpt_level),
      greater_downtown = case_when(
        str_sub(GEOID, 6, 11) %in% westside_tracts_2020 ~ "Westside",
        str_sub(GEOID, 6, 11) %in% downtown_core_tracts ~ "Downtown Core",
        GEOID %in% downtown_core_blocks_2020 ~ "Downtown Core",
        TRUE ~ "Other"
      ),
      SPA = factor(str_to_title(SPA)),
      SPA = fct_relevel(SPA, "Downtown", after = 0),
      greater_downtown = factor(
        greater_downtown, levels = c("Downtown Core", "Westside", "Other"))
    )
  
  save(subdiv_2020, block_2020, file = "decennial_2020.Rdata")

} else {
  
  load("decennial_2020.Rdata")
  
}

The data is sliced three ways below. The top section of the table below is the subdivision data. Cuyahoga County has 1.3 million people with Cleveland’s at 372,624. The second section groups the block-level data by SPA. The Downtown SPA had 13,302 people. This matches the data table on Cleveland Open Data. The Downtown Core defined by Urban Partners, which included portions of the Central, Ohio City, and Cuyahoga Valley SPAs, had 18,708 people.

The map on the second tab shows Cuyahoga County and all of its subdivisions. Cleveland is the largest, and each of its SPAs are broken out. The Downtown SPA is highlighted. The Urban Partners extensions to downtown aren’t shown.

2020 Population Estimates for Cleveland and Vicinity
	Population
Cuyahoga County Subdivisions
Cleveland	372,624
Other	892,193
Total	1,264,817
Cleveland Neighborhoods
Downtown	13,302
Bellaire-Puritas	13,823
Broadway-Slavic Village	19,022
Brooklyn Centre	8,315
Buckeye Shaker	11,419
Central	11,955
Clark Fulton	7,625
Collinwood Nottingham	9,616
Cudell	9,115
Cuyahoga Valley	1,293
Detroit-Shoreway	11,326
Edgewater	6,000
Euclid Green	5,051
Fairfax	5,167
Glenville	21,137
Goodrich-Kirtland Park	3,955
Hopkins	534
Hough	9,702
Jefferson	17,351
Kamms Corners	24,312
Kinsman	5,876
Lee-Harvard	9,770
Lee-Seville	4,171
Mount Pleasant	14,015
North Shore Collinwood	14,928
Ohio City	9,219
Old Brooklyn	32,315
Saint Clair-Superior	5,139
Stockyards	9,522
Tremont	7,798
Union-Miles Park	15,625
University Circle	9,620
West Boulevard	18,981
Woodland Hills	5,625
Total	372,624
Greater Downtown
Downtown Core	18,708
Westside	18,407
Other	335,509
Total	372,624

2010

Unfortunately, pulling 2010 and 2000 isn’t as simple as changing the year parameter in the API calls because they use a different file, Summary File 1.

sf1_2010_vars <-
  tidycensus::load_variables(2010, "sf1") |>
  filter(
    concept %in% c("HISPANIC OR LATINO ORIGIN BY RACE", "SEX BY AGE"),
    !name %in% c("P005002", "P012001", "P012002", "P012026"),
    !str_detect(label, "Total!!Hispanic or Latino!!"),
    !str_detect(name, "^PCT012")
  )

I’ll prepare the variables the same way as with 2020.

Show the code

sf1_2010_vars <-
  sf1_2010_vars |>
  mutate(
    label = str_remove(label, "(Total!!Male!!)|(Total!!Female!!)"),
    label = case_when(
      str_detect(label, "White") ~ "White",
      str_detect(label, "Black") ~ "Black",
      str_detect(label, "Asian") ~ "Asian",
      str_detect(label, "American Indian") ~ "American Indian",
      str_detect(label, "Native Hawaiian") ~ "Pacific Islander",
      str_detect(label, "Some Other Race") ~ "Other",
      str_detect(label, "Two or More Races") ~ "Two or more races",
      str_detect(label, "Hispanic") ~ "Hispanic",
      str_detect(label, "Total") ~ "Total",
      TRUE ~ label,
    ),
    rpt_group = case_when(
      name == "P005001" ~ "Total",
      between(name, "P005003", "P005010") ~ "Race/ethnicity",
      TRUE ~ "Age"
    ),
    rpt_level = case_when(
      label %in% c("White", "Black", "Hispanic", "Asian", "Total", "Other") ~ label,
      label %in% c("American Indian", "Pacific Islander", "Two or more races") ~ "Other",
      label %in% c("Under 5 years", "5 to 9 years", "10 to 14 years") ~ "Under 15 yrs",
      between(label, "15 to 17 years", "22 to 24 years") ~ "15 to 24 yrs",
      label %in% c("25 to 29 years", "30 to 34 years") ~ "25 to 34 yrs",
      label %in% c("35 to 39 years", "40 to 44 years") ~ "35 to 44 yrs",
      label %in% c("45 to 49 years", "50 to 54 years") ~ "45 to 54 yrs",
      between(label, "55 to 59 years", "62 to 64 years") ~ "55 to 64 yrs",
      between(label, "65 and 66 years", "85 years and over") ~ "65+ yrs"
    )
  ) |>
  select(variable = name, label, rpt_group, rpt_level)

sf1_2010_vars

# A tibble: 55 × 4
   variable label             rpt_group      rpt_level   
   <chr>    <chr>             <chr>          <chr>       
 1 P005001  Total             Total          Total       
 2 P005003  White             Race/ethnicity White       
 3 P005004  Black             Race/ethnicity Black       
 4 P005005  American Indian   Race/ethnicity Other       
 5 P005006  Asian             Race/ethnicity Asian       
 6 P005007  Pacific Islander  Race/ethnicity Other       
 7 P005008  Other             Race/ethnicity Other       
 8 P005009  Two or more races Race/ethnicity Other       
 9 P005010  Hispanic          Race/ethnicity Hispanic    
10 P012003  Under 5 years     Age            Under 15 yrs
# ℹ 45 more rows

Request the data from the API. This time I cannot join to cleve_blocks to get precise mappings of census blocks to SPAs. Instead, I’ll join to the cleve_neigh shape file to spatially join to the SPAs. This turns out to be almost as good, but not perfect.

Show the code

# Tract definition are same for 2010.
westside_tracts_2010 <- westside_tracts_2020

downtown_core_tracts_2010 <- c(
  "103300", "107101", "107701", "107802", "109301")
downtown_core_blocks_2010 <- 
  paste0("39035108701", c("3000", "3001", "3002", "3003", "3004"))

if (USE_API) {

  subdiv_2010 <- 
    get_decennial( 
      geography = "county subdivision",
      sumfile = "sf1",
      variables = sf1_2010_vars$variable,
      state = "OH",
      county = "Cuyahoga",
      geometry = TRUE, 
      year = 2010
    ) |> 
    inner_join(sf1_2010_vars, by = "variable") |>
    summarize(
      .by = c(GEOID, NAME, geometry, rpt_group, rpt_level),
      value = sum(value)
    ) |>
    mutate(
      rpt_level = my_rpt_relevel(rpt_level),
      NAME = str_remove_all(NAME, "(, Cuyahoga County, Ohio)|(village)|(city)"),
      NAME = str_trim(NAME)
    )
  
  # tract_2010 <- 
  #   get_decennial( 
  #     geography = "tract",
  #     sumfile = "sf1",
  #     variables = sf1_2010_vars$variable,
  #     state = "OH",
  #     county = "Cuyahoga",
  #     geometry = TRUE, 
  #     year = 2010
  #   ) |> 
  #   inner_join(sf1_2010_vars, by = "variable") |>
  #   summarize(
  #     .by = c(GEOID, NAME, geometry, rpt_group, rpt_level),
  #     value = sum(value)
  #   ) |>
  #   mutate(rpt_level = my_rpt_relevel(rpt_level))

  block_2010_0 <- 
    get_decennial( 
      geography = "block",
      sumfile = "sf1",
      variables = sf1_2010_vars$variable,
      state = "OH",
      county = "Cuyahoga",
      geometry = TRUE, 
      year = 2010
    ) |> 
    inner_join(sf1_2010_vars, by = "variable") |>
    summarize(
      .by = c(GEOID, NAME, geometry, rpt_group, rpt_level),
      value = sum(value)
    ) |>
    mutate(rpt_level = my_rpt_relevel(rpt_level))

  block_2010 <-
    st_join(cleve_neigh, st_centroid(block_2010_0), join = st_contains) |>
    mutate(
      rpt_level = my_rpt_relevel(rpt_level),
      greater_downtown = case_when(
        str_sub(GEOID, 6, 11) %in% westside_tracts_2010 ~ "Westside",
        str_sub(GEOID, 6, 11) %in% downtown_core_tracts_2010 ~ "Downtown Core",
        GEOID %in% downtown_core_blocks_2010 ~ "Downtown Core",
        TRUE ~ "Other"
      ),
      SPA = factor(str_to_title(SPA)),
      SPA = fct_relevel(SPA, "Downtown", after = 0),
      greater_downtown = factor(
        greater_downtown, levels = c("Downtown Core", "Westside", "Other"))
    ) |>
    select(GEOID, NAME, geometry, rpt_group, rpt_level, value, SPA, greater_downtown)
  
  save(subdiv_2010, block_2010, file = "decennial_2010.Rdata")

} else {
  
  load("decennial_2010.Rdata")
  
}

This time the sum of the neighborhoods, 395,601, doesn’t quite equal the city total, 396,815. There must be city blocks whose centers are not captured in the shapes in cleve_neigh. Comparing my values to those in the data table on Cleveland Open Data, the largest differences are in Euclid Green, Kamm’s Corners, and Hopkins. I haven’t thought of a good way to fix this, so I’m settling for “close enough”.

The Downtown SPA population of 9,464 does match the value reported in Cleveland Open Data. It was quite a bit lower than 2020 (13,302). My Downtown Core population of 15,156 is slightly different from Urban Partner’s value of 15,330. The Westside population does match though.

2010 Population Estimates for Cleveland and Vicinity
	Population
Cuyahoga County Subdivisions
Cleveland	396,815
Other	883,307
Total	1,280,122
Cleveland Neighborhoods
Downtown	9,464
Bellaire-Puritas	13,380
Broadway-Slavic Village	22,331
Brooklyn Centre	8,948
Buckeye Shaker	12,470
Central	12,306
Clark Fulton	8,509
Collinwood Nottingham	11,542
Cudell	9,295
Cuyahoga Valley	1,378
Detroit-Shoreway	11,577
Edgewater	5,851
Euclid Green	4,873
Fairfax	6,239
Glenville	27,394
Goodrich-Kirtland Park	4,238
Hopkins	646
Hough	11,490
Jefferson	16,548
Kamms Corners	24,097
Kinsman	6,966
Lee-Harvard	10,326
Lee-Seville	4,477
Mount Pleasant	17,320
North Shore Collinwood	15,768
Ohio City	8,396
Old Brooklyn	32,009
Saint Clair-Superior	6,876
Stockyards	10,411
Tremont	7,975
Union-Miles Park	19,004
University Circle	7,939
West Boulevard	18,880
Woodland Hills	6,678
Total	395,601
Greater Downtown
Downtown Core	15,156
Westside	18,433
Other	362,012
Total	395,601

2000

2000 is similar to 2010 in that it uses Summary File 1.

sf1_2000_vars <-
  tidycensus::load_variables(2000, "sf1") |>
  filter(
    concept %in% c(
      "HISPANIC OR LATINO, AND NOT HISPANIC OR LATINO BY RACE [73]", 
      "SEX BY AGE [49]"
    ),
    !name %in% c("P004003", "P004004", "P012001", "P012002", "P012026"),
    !str_detect(label, "Population of two or more races!!"),
    !str_detect(name, "^PCT013")
  )

Same process: prepare the variables.

Show the code

sf1_2000_vars <-
  sf1_2000_vars |>
  mutate(
    label = str_remove(label, "(Total!!Male!!)|(Total!!Female!!)"),
    label = case_when(
      str_detect(label, "White") ~ "White",
      str_detect(label, "Black") ~ "Black",
      str_detect(label, "Asian") ~ "Asian",
      str_detect(label, "American Indian") ~ "American Indian",
      str_detect(label, "Native Hawaiian") ~ "Pacific Islander",
      str_detect(label, "Some Other Race") ~ "Other",
      str_detect(label, "Two or More Races") ~ "Two or more races",
      str_detect(label, "Hispanic") ~ "Hispanic",
      str_detect(label, "Total") ~ "Total",
      TRUE ~ label,
    ),
    rpt_group = case_when(
      name == "P004001" ~ "Total",
      between(name, "P004002", "P004011") ~ "Race/ethnicity",
      TRUE ~ "Age"
    ),
    rpt_level = case_when(
      label %in% c("White", "Black", "Hispanic", "Asian", "Total", "Other") ~ label,
      label %in% c("American Indian", "Pacific Islander", "Two or more races") ~ "Other",
      label %in% c("Under 5 years", "5 to 9 years", "10 to 14 years") ~ "Under 15 yrs",
      between(label, "15 to 17 years", "22 to 24 years") ~ "15 to 24 yrs",
      label %in% c("25 to 29 years", "30 to 34 years") ~ "25 to 34 yrs",
      label %in% c("35 to 39 years", "40 to 44 years") ~ "35 to 44 yrs",
      label %in% c("45 to 49 years", "50 to 54 years") ~ "45 to 54 yrs",
      between(label, "55 to 59 years", "62 to 64 years") ~ "55 to 64 yrs",
      between(label, "65 and 66 years", "85 years and over") ~ "65+ yrs"
    )
  ) |>
  select(variable = name, label, rpt_group, rpt_level)

sf1_2010_vars

# A tibble: 55 × 4
   variable label             rpt_group      rpt_level   
   <chr>    <chr>             <chr>          <chr>       
 1 P005001  Total             Total          Total       
 2 P005003  White             Race/ethnicity White       
 3 P005004  Black             Race/ethnicity Black       
 4 P005005  American Indian   Race/ethnicity Other       
 5 P005006  Asian             Race/ethnicity Asian       
 6 P005007  Pacific Islander  Race/ethnicity Other       
 7 P005008  Other             Race/ethnicity Other       
 8 P005009  Two or more races Race/ethnicity Other       
 9 P005010  Hispanic          Race/ethnicity Hispanic    
10 P012003  Under 5 years     Age            Under 15 yrs
# ℹ 45 more rows

Request the data from the API. I’ll used the cleve_neigh shape file again to identify the SPAs. Tract and block identifiers can change from census to census, so I had to make some changes to the Downtown Core definition. I used the same block identifiers for Urban Partners’ definitions, but they did not include 2020 in their report, so I’m not sure how much this differs.

Show the code

# Tract definition are same for 2000.
westside_tracts_2000 <- westside_tracts_2020

downtown_core_tracts_2000 <- c(
  "107100", "107200", "107300", "107400", "107500", "107600", "107700",
  "107800", "107900", "109200")

downtown_core_blocks_2000 <- 
  paste0("39035108701", c("3000", "3001", "3002", "3003", "3004"))

if (USE_API) {

  subdiv_2000_0 <- 
    get_decennial( 
      geography = "county subdivision",
      sumfile = "sf1",
      variables = sf1_2000_vars$variable,
      state = "OH",
      county = "Cuyahoga",
      geometry = FALSE, # no county subdivision geography in 2000
      year = 2000
    ) |> 
    inner_join(sf1_2000_vars, by = "variable") |>
    summarize(
      .by = c(GEOID, NAME, rpt_group, rpt_level),
      value = sum(value)
    ) |>
    mutate(
      rpt_level = my_rpt_relevel(rpt_level),
      NAME = str_remove_all(NAME, "(, Cuyahoga County, Ohio)|(village)|(city)"),
      NAME = str_trim(NAME)
    )
  
  # No geometry for 2000? No problem? I'll use the 2010 geometry and replace the 
  # values with 2000.
  subdiv_2000_1 <- 
    subdiv_2000_0 |> 
    as_tibble() |> 
    select(GEOID, rpt_group, rpt_level, value)
  
  subdiv_2000 <- 
    subdiv_2010 |>
    select(-value) |>
    inner_join(subdiv_2000_1, by = c("GEOID", "rpt_group", "rpt_level"))
  
  # tract_2000 <- 
  #   get_decennial( 
  #     geography = "tract",
  #     sumfile = "sf1",
  #     variables = sf1_2000_vars$variable,
  #     state = "OH",
  #     county = "Cuyahoga",
  #     geometry = TRUE, 
  #     year = 2000
  #   ) |> 
  #   inner_join(sf1_2000_vars, by = "variable") |>
  #   summarize(
  #     .by = c(GEOID, NAME, geometry, rpt_group, rpt_level),
  #     value = sum(value)
  #   ) |>
  #   mutate(rpt_level = my_rpt_relevel(rpt_level))

  block_2000_0 <- 
    get_decennial( 
      geography = "block",
      sumfile = "sf1",
      variables = sf1_2000_vars$variable,
      state = "OH",
      county = "Cuyahoga",
      geometry = TRUE, 
      year = 2000
    ) |> 
    inner_join(sf1_2000_vars, by = "variable") |>
    summarize(
      .by = c(GEOID, NAME, geometry, rpt_group, rpt_level),
      value = sum(value)
    ) |>
    mutate(rpt_level = my_rpt_relevel(rpt_level))

  block_2000 <-
    st_join(cleve_neigh, st_centroid(block_2000_0), join = st_contains) |>
    mutate(
      rpt_level = my_rpt_relevel(rpt_level),
      greater_downtown = case_when(
        str_sub(GEOID, 6, 11) %in% westside_tracts_2000 ~ "Westside",
        str_sub(GEOID, 6, 11) %in% downtown_core_tracts_2000 ~ "Downtown Core",
        GEOID %in% downtown_core_blocks_2000 ~ "Downtown Core",
        TRUE ~ "Other"
      ),
      SPA = factor(str_to_title(SPA)),
      SPA = fct_relevel(SPA, "Downtown", after = 0),
      greater_downtown = factor(
        greater_downtown, levels = c("Downtown Core", "Westside", "Other"))
    ) |>
    select(GEOID, NAME, geometry, rpt_group, rpt_level, value, SPA, greater_downtown)
  
  save(subdiv_2000, block_2000, file = "decennial_2000.Rdata")

} else {
  
  load("decennial_2000.Rdata")
  
}

As with 2010, the sum of the neighborhoods, 477,107, doesn’t quite match the city value, 478,403, but that is still pretty close. Wow, 478,403 people in 2000, that’s 100K more than 2020. On the other hand, only 6,310 people lived Downtown. The Downtown resurgence of does not seem to be a recent phenomena.

2000 Population Estimates for Cleveland and Vicinity
	Population
Cuyahoga County Subdivisions
Cleveland	478,403
Other	915,575
Total	1,393,978
Cleveland Neighborhoods
Downtown	6,310
Bellaire-Puritas	14,520
Broadway-Slavic Village	30,652
Brooklyn Centre	10,155
Buckeye Shaker	16,063
Central	11,568
Clark Fulton	10,672
Collinwood Nottingham	15,874
Cudell	10,630
Cuyahoga Valley	1,307
Detroit-Shoreway	13,917
Edgewater	6,360
Euclid Green	6,169
Fairfax	8,447
Glenville	39,941
Goodrich-Kirtland Park	4,580
Hopkins	338
Hough	14,734
Jefferson	18,266
Kamms Corners	25,256
Kinsman	10,256
Lee-Harvard	11,665
Lee-Seville	5,595
Mount Pleasant	24,013
North Shore Collinwood	18,346
Ohio City	8,726
Old Brooklyn	34,169
Saint Clair-Superior	11,534
Stockyards	12,076
Tremont	9,317
Union-Miles Park	26,539
University Circle	9,386
West Boulevard	20,492
Woodland Hills	9,234
Total	477,107
Greater Downtown
Downtown Core	8,412
Westside	15,154
Other	453,541
Total	477,107

2023 (ACS)

The 2023 American Community Survey publishes a 1-year and 5-year average. The 1-year survey might be helpful, but it doesn’t have block-level data. I’ll download the subdivision file and check in on Cleveland as a whole.

acs1_2023_vars <-
  tidycensus::load_variables(2023, "acs1") |>
  filter(
    concept %in% c("Sex by Age", "Hispanic or Latino Origin by Race"),
    # between(name, "B01001_001E_001N", "P2_011N"),
    !name %in% c("B01001_002", "B01001_026", "B03002_001", "B03002_002",
                 "B03002_010", "B03002_011"),
    name <= "B03002_012"
  )

Same variable prep.

Show the code

acs1_2023_vars <-
  acs1_2023_vars |>
  mutate(
    label = str_remove_all(label, "(Estimate!!Total:!!)|(Male:!!)|(Female:!!)"),
    label = case_when(
      str_detect(label, "White") ~ "White",
      str_detect(label, "Black") ~ "Black",
      str_detect(label, "Asian") ~ "Asian",
      str_detect(label, "American Indian") ~ "American Indian",
      str_detect(label, "Native Hawaiian") ~ "Pacific Islander",
      str_detect(label, "Some other race") ~ "Other",
      str_detect(label, "Two or more races") ~ "Two or more races",
      str_detect(label, "Hispanic") ~ "Hispanic",
      str_detect(label, "Total") ~ "Total",
      TRUE ~ label,
    ),
    rpt_group = case_when(
      name == "B01001_001" ~ "Total",
      between(name, "B03002_003", "B03002_012") ~ "Race/ethnicity",
      TRUE ~ "Age"
    ),
    rpt_level = case_when(
      label %in% c("White", "Black", "Hispanic", "Asian", "Total", "Other") ~ label,
      label %in% c("American Indian", "Pacific Islander", "Two or more races") ~ "Other",
      label %in% c("Under 5 years", "5 to 9 years", "10 to 14 years") ~ "Under 15 yrs",
      between(label, "15 to 17 years", "22 to 24 years") ~ "15 to 24 yrs",
      label %in% c("25 to 29 years", "30 to 34 years") ~ "25 to 34 yrs",
      label %in% c("35 to 39 years", "40 to 44 years") ~ "35 to 44 yrs",
      label %in% c("45 to 49 years", "50 to 54 years") ~ "45 to 54 yrs",
      between(label, "55 to 59 years", "62 to 64 years") ~ "55 to 64 yrs",
      between(label, "65 and 66 years", "85 years and over") ~ "65+ yrs"
    )
  ) |>
  select(variable = name, label, rpt_group, rpt_level)

acs1_2023_vars

# A tibble: 55 × 4
   variable   label           rpt_group rpt_level   
   <chr>      <chr>           <chr>     <chr>       
 1 B01001_001 Total           Total     Total       
 2 B01001_003 Under 5 years   Age       Under 15 yrs
 3 B01001_004 5 to 9 years    Age       Under 15 yrs
 4 B01001_005 10 to 14 years  Age       Under 15 yrs
 5 B01001_006 15 to 17 years  Age       15 to 24 yrs
 6 B01001_007 18 and 19 years Age       15 to 24 yrs
 7 B01001_008 20 years        Age       15 to 24 yrs
 8 B01001_009 21 years        Age       15 to 24 yrs
 9 B01001_010 22 to 24 years  Age       15 to 24 yrs
10 B01001_011 25 to 29 years  Age       25 to 34 yrs
# ℹ 45 more rows

Request the data from the API.

Show the code

if (USE_API) {

  subdiv_2023 <- 
    get_acs( 
      geography = "county subdivision",
      sumfile = "acs1",
      variables = acs1_2023_vars$variable,
      state = "OH",
      county = "Cuyahoga",
      geometry = FALSE, # no geo file for ACS-1yr
      year = 2023
    ) |> 
    inner_join(acs1_2023_vars, by = "variable") |>
    summarize(
      .by = c(GEOID, NAME, rpt_group, rpt_level),
      value = sum(estimate)
    ) |>
    mutate(
      rpt_level = my_rpt_relevel(rpt_level),
      NAME = str_remove_all(NAME, "(, Cuyahoga County, Ohio)|(village)|(city)"),
      NAME = str_trim(NAME)
    )
  
  # tract_2023 <- 
  #   get_acs( 
  #     geography = "tract",
  #     sumfile = "acs1",
  #     variables = acs1_2023_vars$variable,
  #     state = "OH",
  #     county = "Cuyahoga",
  #     geometry = FALSE, 
  #     year = 2023
  #   ) |> 
  #   inner_join(acs1_2023_vars, by = "variable") |>
  #   summarize(
  #     .by = c(GEOID, NAME, rpt_group, rpt_level),
  #     value = sum(estimate)
  #   ) |>
  #   mutate(rpt_level = my_rpt_relevel(rpt_level))

  save(subdiv_2023, file = "acs1yr_2023.Rdata")

} else {
  
  load("acs1yr_2023.Rdata")
  
}

Cleveland’s population has continued to decline, down to 367,523 from 372,624 in 2020.

2023 Population Estimates for Cleveland and Vicinity
	Population
Cleveland	367,523
Other	881,895
Total	1,249,418

Population Trends

Downtown Cleveland Inc. defined “Greater Downtown Cleveland” in their report as the “Downtown Core” on the east side of the Cuyahoga and “Westside” to west. Their Downtown Core is bounded by about E. 30th St. and Woodland Ave to the South.⁹. The Westside is bounded mostly by W. 65th St and Clark Avenue to the south. I’m not sure how they came up with this definition. Maybe it was with census blocks. Their Greater Downtown includes 37,115 people with 18,708 in the Downtown Core. I’ll stick with the neighborhoods definition.

Let’s start with a neighborhood table showing the population changes since 2000. The Downtown population was only 8,227 in 2000 - 2% of Cleveland’s 478,403 population. Cleveland’s population fell 17% over the next decade. Hit hardest the east side neighborhoods such as Buckeye-Woodhill, Glenville, Kinsman, Lee-Seville, and St.Clair-Superior, all of which lost over 30% of their population. However, Downtown experienced a 15% population increase over this period.

Changing Total Population in Cleveland, 2000-2023
Decennial and ACS censuses
	2000	2010	Δ	%	2020	Δ	%	2023	Δ	%
Bellaire-Puritas	14,520	13,380	−1,140	−8%	13,823	443	3%	NA	NA	NA
Broadway-Slavic Village	30,652	22,331	−8,321	−27%	19,022	−3,309	−15%	NA	NA	NA
Brooklyn Centre	10,155	8,948	−1,207	−12%	8,315	−633	−7%	NA	NA	NA
Buckeye Shaker	16,063	12,470	−3,593	−22%	11,419	−1,051	−8%	NA	NA	NA
Central	11,568	12,306	738	6%	11,955	−351	−3%	NA	NA	NA
Clark Fulton	10,672	8,509	−2,163	−20%	7,625	−884	−10%	NA	NA	NA
Collinwood Nottingham	15,874	11,542	−4,332	−27%	9,616	−1,926	−17%	NA	NA	NA
Cudell	10,630	9,295	−1,335	−13%	9,115	−180	−2%	NA	NA	NA
Cuyahoga Valley	1,307	1,378	71	5%	1,293	−85	−6%	NA	NA	NA
Detroit-Shoreway	13,917	11,577	−2,340	−17%	11,326	−251	−2%	NA	NA	NA
Downtown	6,310	9,464	3,154	50%	13,302	3,838	41%	NA	NA	NA
Edgewater	6,360	5,851	−509	−8%	6,000	149	3%	NA	NA	NA
Euclid Green	6,169	4,873	−1,296	−21%	5,051	178	4%	NA	NA	NA
Fairfax	8,447	6,239	−2,208	−26%	5,167	−1,072	−17%	NA	NA	NA
Glenville	39,941	27,394	−12,547	−31%	21,137	−6,257	−23%	NA	NA	NA
Goodrich-Kirtland Park	4,580	4,238	−342	−7%	3,955	−283	−7%	NA	NA	NA
Hopkins	338	646	308	91%	534	−112	−17%	NA	NA	NA
Hough	14,734	11,490	−3,244	−22%	9,702	−1,788	−16%	NA	NA	NA
Jefferson	18,266	16,548	−1,718	−9%	17,351	803	5%	NA	NA	NA
Kamms Corners	25,256	24,097	−1,159	−5%	24,312	215	1%	NA	NA	NA
Kinsman	10,256	6,966	−3,290	−32%	5,876	−1,090	−16%	NA	NA	NA
Lee-Harvard	11,665	10,326	−1,339	−11%	9,770	−556	−5%	NA	NA	NA
Lee-Seville	5,595	4,477	−1,118	−20%	4,171	−306	−7%	NA	NA	NA
Mount Pleasant	24,013	17,320	−6,693	−28%	14,015	−3,305	−19%	NA	NA	NA
North Shore Collinwood	18,346	15,768	−2,578	−14%	14,928	−840	−5%	NA	NA	NA
Ohio City	8,726	8,396	−330	−4%	9,219	823	10%	NA	NA	NA
Old Brooklyn	34,169	32,009	−2,160	−6%	32,315	306	1%	NA	NA	NA
Saint Clair-Superior	11,534	6,876	−4,658	−40%	5,139	−1,737	−25%	NA	NA	NA
Stockyards	12,076	10,411	−1,665	−14%	9,522	−889	−9%	NA	NA	NA
Tremont	9,317	7,975	−1,342	−14%	7,798	−177	−2%	NA	NA	NA
Union-Miles Park	26,539	19,004	−7,535	−28%	15,625	−3,379	−18%	NA	NA	NA
University Circle	9,386	7,939	−1,447	−15%	9,620	1,681	21%	NA	NA	NA
West Boulevard	20,492	18,880	−1,612	−8%	18,981	101	1%	NA	NA	NA
Woodland Hills	9,234	6,678	−2,556	−28%	5,625	−1,053	−16%	NA	NA	NA
Total	478,403	396,815	−81,588	−17%	372,624	−24,191	−6%	367,523	−5,101	−1%

Race/ethnicity

Show the code

neigh_gt("Race/ethnicity", "Black")

Changing Black Population in Cleveland, 2000-2023
Decennial and ACS censuses
	2000	2010	Δ	%	2020	Δ	%	2023	Δ	%
Bellaire-Puritas	3,092	3,356	264	9%	3,215	−141	−4%	NA	NA	NA
Broadway-Slavic Village	7,910	11,576	3,666	46%	10,225	−1,351	−12%	NA	NA	NA
Brooklyn Centre	1,102	1,751	649	59%	1,724	−27	−2%	NA	NA	NA
Buckeye Shaker	12,783	10,064	−2,719	−21%	8,583	−1,481	−15%	NA	NA	NA
Central	10,926	11,505	579	5%	9,997	−1,508	−13%	NA	NA	NA
Clark Fulton	1,000	1,441	441	44%	1,378	−63	−4%	NA	NA	NA
Collinwood Nottingham	12,261	10,021	−2,240	−18%	8,329	−1,692	−17%	NA	NA	NA
Cudell	1,724	2,976	1,252	73%	2,879	−97	−3%	NA	NA	NA
Cuyahoga Valley	609	566	−43	−7%	260	−306	−54%	NA	NA	NA
Detroit-Shoreway	2,487	2,867	380	15%	2,309	−558	−19%	NA	NA	NA
Downtown	3,448	4,140	692	20%	3,430	−710	−17%	NA	NA	NA
Edgewater	742	1,194	452	61%	867	−327	−27%	NA	NA	NA
Euclid Green	5,607	4,524	−1,083	−19%	4,472	−52	−1%	NA	NA	NA
Fairfax	8,002	5,932	−2,070	−26%	4,637	−1,295	−22%	NA	NA	NA
Glenville	38,918	26,516	−12,402	−32%	19,524	−6,992	−26%	NA	NA	NA
Goodrich-Kirtland Park	849	985	136	16%	908	−77	−8%	NA	NA	NA
Hopkins	9	131	122	1,356%	148	17	13%	NA	NA	NA
Hough	14,131	10,896	−3,235	−23%	8,453	−2,443	−22%	NA	NA	NA
Jefferson	1,663	2,604	941	57%	3,106	502	19%	NA	NA	NA
Kamms Corners	1,702	2,503	801	47%	2,229	−274	−11%	NA	NA	NA
Kinsman	9,903	6,696	−3,207	−32%	5,480	−1,216	−18%	NA	NA	NA
Lee-Harvard	11,380	10,012	−1,368	−12%	9,263	−749	−7%	NA	NA	NA
Lee-Seville	5,367	4,317	−1,050	−20%	3,914	−403	−9%	NA	NA	NA
Mount Pleasant	23,394	16,817	−6,577	−28%	13,175	−3,642	−22%	NA	NA	NA
North Shore Collinwood	9,065	10,359	1,294	14%	10,310	−49	−0%	NA	NA	NA
Ohio City	2,159	2,718	559	26%	2,082	−636	−23%	NA	NA	NA
Old Brooklyn	836	2,337	1,501	180%	3,657	1,320	56%	NA	NA	NA
Saint Clair-Superior	8,628	5,347	−3,281	−38%	3,743	−1,604	−30%	NA	NA	NA
Stockyards	1,043	1,729	686	66%	1,605	−124	−7%	NA	NA	NA
Tremont	1,669	1,721	52	3%	1,166	−555	−32%	NA	NA	NA
Union-Miles Park	25,523	18,339	−7,184	−28%	14,566	−3,773	−21%	NA	NA	NA
University Circle	2,767	1,849	−918	−33%	1,696	−153	−8%	NA	NA	NA
West Boulevard	1,964	3,589	1,625	83%	4,234	645	18%	NA	NA	NA
Woodland Hills	8,839	6,409	−2,430	−27%	5,249	−1,160	−18%	NA	NA	NA
Total	241,512	208,208	−33,304	−14%	176,813	−31,395	−15%	169,138	−7,675	−4%

Show the code

neigh_gt("Race/ethnicity", "White")

Changing White Population in Cleveland, 2000-2023
Decennial and ACS censuses
	2000	2010	Δ	%	2020	Δ	%	2023	Δ	%
Bellaire-Puritas	9,761	7,479	−2,282	−23%	6,050	−1,429	−19%	NA	NA	NA
Broadway-Slavic Village	20,498	8,822	−11,676	−57%	5,988	−2,834	−32%	NA	NA	NA
Brooklyn Centre	5,937	3,962	−1,975	−33%	3,033	−929	−23%	NA	NA	NA
Buckeye Shaker	2,420	1,602	−818	−34%	1,707	105	7%	NA	NA	NA
Central	320	418	98	31%	522	104	25%	NA	NA	NA
Clark Fulton	4,449	2,789	−1,660	−37%	1,942	−847	−30%	NA	NA	NA
Collinwood Nottingham	3,099	1,145	−1,954	−63%	736	−409	−36%	NA	NA	NA
Cudell	6,220	3,813	−2,407	−39%	3,279	−534	−14%	NA	NA	NA
Cuyahoga Valley	643	714	71	11%	893	179	25%	NA	NA	NA
Detroit-Shoreway	7,516	5,257	−2,259	−30%	5,591	334	6%	NA	NA	NA
Downtown	2,380	4,061	1,681	71%	7,550	3,489	86%	NA	NA	NA
Edgewater	4,926	3,865	−1,061	−22%	4,089	224	6%	NA	NA	NA
Euclid Green	390	223	−167	−43%	186	−37	−17%	NA	NA	NA
Fairfax	221	130	−91	−41%	195	65	50%	NA	NA	NA
Glenville	328	238	−90	−27%	473	235	99%	NA	NA	NA
Goodrich-Kirtland Park	1,825	1,479	−346	−19%	1,077	−402	−27%	NA	NA	NA
Hopkins	299	419	120	40%	229	−190	−45%	NA	NA	NA
Hough	272	245	−27	−10%	417	172	70%	NA	NA	NA
Jefferson	14,155	10,246	−3,909	−28%	8,676	−1,570	−15%	NA	NA	NA
Kamms Corners	21,777	18,685	−3,092	−14%	17,643	−1,042	−6%	NA	NA	NA
Kinsman	168	101	−67	−40%	118	17	17%	NA	NA	NA
Lee-Harvard	99	63	−36	−36%	90	27	43%	NA	NA	NA
Lee-Seville	102	32	−70	−69%	48	16	50%	NA	NA	NA
Mount Pleasant	146	112	−34	−23%	166	54	48%	NA	NA	NA
North Shore Collinwood	8,650	4,853	−3,797	−44%	3,659	−1,194	−25%	NA	NA	NA
Ohio City	4,327	3,657	−670	−15%	5,087	1,430	39%	NA	NA	NA
Old Brooklyn	30,167	24,066	−6,101	−20%	19,307	−4,759	−20%	NA	NA	NA
Saint Clair-Superior	1,904	1,028	−876	−46%	782	−246	−24%	NA	NA	NA
Stockyards	7,203	4,614	−2,589	−36%	3,418	−1,196	−26%	NA	NA	NA
Tremont	4,899	4,094	−805	−16%	4,583	489	12%	NA	NA	NA
Union-Miles Park	502	263	−239	−48%	299	36	14%	NA	NA	NA
University Circle	5,135	4,279	−856	−17%	4,722	443	10%	NA	NA	NA
West Boulevard	13,550	9,098	−4,452	−33%	6,882	−2,216	−24%	NA	NA	NA
Woodland Hills	201	139	−62	−31%	110	−29	−21%	NA	NA	NA
Total	185,641	132,710	−52,931	−29%	119,547	−13,163	−10%	124,183	4,636	4%

Show the code

neigh_gt("Race/ethnicity", "Hispanic")

Changing Hispanic Population in Cleveland, 2000-2023
Decennial and ACS censuses
	2000	2010	Δ	%	2020	Δ	%	2023	Δ	%
Bellaire-Puritas	1,263	1,749	486	38%	2,967	1,218	70%	NA	NA	NA
Broadway-Slavic Village	2,077	1,206	−871	−42%	1,664	458	38%	NA	NA	NA
Brooklyn Centre	3,003	2,869	−134	−4%	2,957	88	3%	NA	NA	NA
Buckeye Shaker	501	178	−323	−64%	281	103	58%	NA	NA	NA
Central	291	205	−86	−30%	962	757	369%	NA	NA	NA
Clark Fulton	5,095	4,013	−1,082	−21%	3,804	−209	−5%	NA	NA	NA
Collinwood Nottingham	455	177	−278	−61%	216	39	22%	NA	NA	NA
Cudell	2,120	1,811	−309	−15%	2,031	220	12%	NA	NA	NA
Cuyahoga Valley	51	49	−2	−4%	73	24	49%	NA	NA	NA
Detroit-Shoreway	3,657	2,886	−771	−21%	2,548	−338	−12%	NA	NA	NA
Downtown	325	313	−12	−4%	707	394	126%	NA	NA	NA
Edgewater	552	507	−45	−8%	510	3	1%	NA	NA	NA
Euclid Green	141	40	−101	−72%	193	153	382%	NA	NA	NA
Fairfax	194	37	−157	−81%	95	58	157%	NA	NA	NA
Glenville	595	202	−393	−66%	356	154	76%	NA	NA	NA
Goodrich-Kirtland Park	656	439	−217	−33%	550	111	25%	NA	NA	NA
Hopkins	17	56	39	229%	105	49	88%	NA	NA	NA
Hough	290	152	−138	−48%	297	145	95%	NA	NA	NA
Jefferson	2,091	2,944	853	41%	3,828	884	30%	NA	NA	NA
Kamms Corners	1,368	1,762	394	29%	2,244	482	27%	NA	NA	NA
Kinsman	161	58	−103	−64%	94	36	62%	NA	NA	NA
Lee-Harvard	160	83	−77	−48%	113	30	36%	NA	NA	NA
Lee-Seville	117	34	−83	−71%	69	35	103%	NA	NA	NA
Mount Pleasant	440	145	−295	−67%	194	49	34%	NA	NA	NA
North Shore Collinwood	517	197	−320	−62%	305	108	55%	NA	NA	NA
Ohio City	2,135	1,720	−415	−19%	1,427	−293	−17%	NA	NA	NA
Old Brooklyn	2,691	4,414	1,723	64%	7,180	2,766	63%	NA	NA	NA
Saint Clair-Superior	943	340	−603	−64%	394	54	16%	NA	NA	NA
Stockyards	3,630	3,645	15	0%	3,888	243	7%	NA	NA	NA
Tremont	2,611	1,873	−738	−28%	1,518	−355	−19%	NA	NA	NA
Union-Miles Park	468	117	−351	−75%	277	160	137%	NA	NA	NA
University Circle	364	179	−185	−51%	554	375	209%	NA	NA	NA
West Boulevard	4,404	5,027	623	14%	6,185	1,158	23%	NA	NA	NA
Woodland Hills	176	54	−122	−69%	113	59	109%	NA	NA	NA
Total	43,648	39,534	−4,114	−9%	48,699	9,165	23%	47,132	−1,567	−3%

Show the code

neigh_gt("Race/ethnicity", "Asian")

Changing Asian Population in Cleveland, 2000-2023
Decennial and ACS censuses
	2000	2010	Δ	%	2020	Δ	%	2023	Δ	%
Bellaire-Puritas	344	335	−9	−3%	645	310	93%	NA	NA	NA
Broadway-Slavic Village	83	52	−31	−37%	33	−19	−37%	NA	NA	NA
Brooklyn Centre	56	53	−3	−5%	83	30	57%	NA	NA	NA
Buckeye Shaker	332	419	87	26%	404	−15	−4%	NA	NA	NA
Central	9	20	11	122%	78	58	290%	NA	NA	NA
Clark Fulton	86	45	−41	−48%	100	55	122%	NA	NA	NA
Collinwood Nottingham	15	9	−6	−40%	15	6	67%	NA	NA	NA
Cudell	523	310	−213	−41%	332	22	7%	NA	NA	NA
Cuyahoga Valley	1	34	33	3,300%	11	−23	−68%	NA	NA	NA
Detroit-Shoreway	177	149	−28	−16%	264	115	77%	NA	NA	NA
Downtown	145	731	586	404%	1,118	387	53%	NA	NA	NA
Edgewater	104	109	5	5%	186	77	71%	NA	NA	NA
Euclid Green	8	6	−2	−25%	6	0	0%	NA	NA	NA
Fairfax	10	50	40	400%	90	40	80%	NA	NA	NA
Glenville	31	34	3	10%	125	91	268%	NA	NA	NA
Goodrich-Kirtland Park	1,243	1,263	20	2%	1,234	−29	−2%	NA	NA	NA
Hopkins	13	8	−5	−38%	28	20	250%	NA	NA	NA
Hough	22	29	7	32%	157	128	441%	NA	NA	NA
Jefferson	300	305	5	2%	728	423	139%	NA	NA	NA
Kamms Corners	358	642	284	79%	1,044	402	63%	NA	NA	NA
Kinsman	9	9	0	0%	10	1	11%	NA	NA	NA
Lee-Harvard	11	12	1	9%	14	2	17%	NA	NA	NA
Lee-Seville	1	2	1	100%	3	1	50%	NA	NA	NA
Mount Pleasant	9	6	−3	−33%	20	14	233%	NA	NA	NA
North Shore Collinwood	71	34	−37	−52%	48	14	41%	NA	NA	NA
Ohio City	59	82	23	39%	218	136	166%	NA	NA	NA
Old Brooklyn	402	414	12	3%	392	−22	−5%	NA	NA	NA
Saint Clair-Superior	30	16	−14	−47%	21	5	31%	NA	NA	NA
Stockyards	114	81	−33	−29%	78	−3	−4%	NA	NA	NA
Tremont	70	75	5	7%	190	115	153%	NA	NA	NA
Union-Miles Park	14	15	1	7%	14	−1	−7%	NA	NA	NA
University Circle	1,100	1,418	318	29%	2,200	782	55%	NA	NA	NA
West Boulevard	483	440	−43	−9%	491	51	12%	NA	NA	NA
Woodland Hills	7	2	−5	−71%	10	8	400%	NA	NA	NA
Total	6,284	7,213	929	15%	10,390	3,177	44%	8,356	−2,034	−20%

Age

Changing Under 15 yrs Population in Cleveland, 2000-2023
Decennial and ACS censuses
	2000	2010	Δ	%	2020	Δ	%	2023	Δ	%
Bellaire-Puritas	3,133	2,754	−379	−12%	NA	NA	NA	NA	NA	NA
Broadway-Slavic Village	8,256	5,373	−2,883	−35%	NA	NA	NA	NA	NA	NA
Brooklyn Centre	2,735	2,106	−629	−23%	NA	NA	NA	NA	NA	NA
Buckeye Shaker	3,471	2,048	−1,423	−41%	NA	NA	NA	NA	NA	NA
Central	4,372	4,717	345	8%	NA	NA	NA	NA	NA	NA
Clark Fulton	3,204	2,102	−1,102	−34%	NA	NA	NA	NA	NA	NA
Collinwood Nottingham	4,556	2,518	−2,038	−45%	NA	NA	NA	NA	NA	NA
Cudell	2,747	2,199	−548	−20%	NA	NA	NA	NA	NA	NA
Cuyahoga Valley	176	129	−47	−27%	NA	NA	NA	NA	NA	NA
Detroit-Shoreway	3,650	2,472	−1,178	−32%	NA	NA	NA	NA	NA	NA
Downtown	267	295	28	10%	NA	NA	NA	NA	NA	NA
Edgewater	800	685	−115	−14%	NA	NA	NA	NA	NA	NA
Euclid Green	1,552	924	−628	−40%	NA	NA	NA	NA	NA	NA
Fairfax	2,079	1,222	−857	−41%	NA	NA	NA	NA	NA	NA
Glenville	10,878	5,908	−4,970	−46%	NA	NA	NA	NA	NA	NA
Goodrich-Kirtland Park	794	569	−225	−28%	NA	NA	NA	NA	NA	NA
Hopkins	67	128	61	91%	NA	NA	NA	NA	NA	NA
Hough	4,032	2,397	−1,635	−41%	NA	NA	NA	NA	NA	NA
Jefferson	4,054	3,382	−672	−17%	NA	NA	NA	NA	NA	NA
Kamms Corners	5,133	4,587	−546	−11%	NA	NA	NA	NA	NA	NA
Kinsman	3,408	1,812	−1,596	−47%	NA	NA	NA	NA	NA	NA
Lee-Harvard	2,051	1,636	−415	−20%	NA	NA	NA	NA	NA	NA
Lee-Seville	1,274	795	−479	−38%	NA	NA	NA	NA	NA	NA
Mount Pleasant	6,306	3,606	−2,700	−43%	NA	NA	NA	NA	NA	NA
North Shore Collinwood	3,867	2,793	−1,074	−28%	NA	NA	NA	NA	NA	NA
Ohio City	1,854	1,440	−414	−22%	NA	NA	NA	NA	NA	NA
Old Brooklyn	6,725	5,822	−903	−13%	NA	NA	NA	NA	NA	NA
Saint Clair-Superior	3,754	1,479	−2,275	−61%	NA	NA	NA	NA	NA	NA
Stockyards	3,456	2,732	−724	−21%	NA	NA	NA	NA	NA	NA
Tremont	2,356	1,410	−946	−40%	NA	NA	NA	NA	NA	NA
Union-Miles Park	7,154	3,680	−3,474	−49%	NA	NA	NA	NA	NA	NA
University Circle	529	247	−282	−53%	NA	NA	NA	NA	NA	NA
West Boulevard	5,259	4,531	−728	−14%	NA	NA	NA	NA	NA	NA
Woodland Hills	3,018	1,697	−1,321	−44%	NA	NA	NA	NA	NA	NA
Total	117,101	80,298	−36,803	−31%	67,636	−12,662	−16%	64,554	−3,082	−5%

Changing 15 to 24 yrs Population in Cleveland, 2000-2023
Decennial and ACS censuses
	2000	2010	Δ	%	2020	Δ	%	2023	Δ	%
Bellaire-Puritas	1,589	1,629	40	3%	NA	NA	NA	NA	NA	NA
Broadway-Slavic Village	4,033	3,419	−614	−15%	NA	NA	NA	NA	NA	NA
Brooklyn Centre	1,471	1,406	−65	−4%	NA	NA	NA	NA	NA	NA
Buckeye Shaker	1,929	1,678	−251	−13%	NA	NA	NA	NA	NA	NA
Central	2,012	2,133	121	6%	NA	NA	NA	NA	NA	NA
Clark Fulton	1,671	1,508	−163	−10%	NA	NA	NA	NA	NA	NA
Collinwood Nottingham	2,104	1,986	−118	−6%	NA	NA	NA	NA	NA	NA
Cudell	1,508	1,503	−5	−0%	NA	NA	NA	NA	NA	NA
Cuyahoga Valley	162	412	250	154%	NA	NA	NA	NA	NA	NA
Detroit-Shoreway	1,932	1,664	−268	−14%	NA	NA	NA	NA	NA	NA
Downtown	1,576	2,445	869	55%	NA	NA	NA	NA	NA	NA
Edgewater	713	690	−23	−3%	NA	NA	NA	NA	NA	NA
Euclid Green	842	671	−171	−20%	NA	NA	NA	NA	NA	NA
Fairfax	1,052	886	−166	−16%	NA	NA	NA	NA	NA	NA
Glenville	5,561	4,386	−1,175	−21%	NA	NA	NA	NA	NA	NA
Goodrich-Kirtland Park	636	569	−67	−11%	NA	NA	NA	NA	NA	NA
Hopkins	38	100	62	163%	NA	NA	NA	NA	NA	NA
Hough	1,880	1,662	−218	−12%	NA	NA	NA	NA	NA	NA
Jefferson	2,111	2,148	37	2%	NA	NA	NA	NA	NA	NA
Kamms Corners	2,283	2,435	152	7%	NA	NA	NA	NA	NA	NA
Kinsman	1,507	1,095	−412	−27%	NA	NA	NA	NA	NA	NA
Lee-Harvard	1,130	1,212	82	7%	NA	NA	NA	NA	NA	NA
Lee-Seville	629	604	−25	−4%	NA	NA	NA	NA	NA	NA
Mount Pleasant	3,198	2,663	−535	−17%	NA	NA	NA	NA	NA	NA
North Shore Collinwood	1,763	1,907	144	8%	NA	NA	NA	NA	NA	NA
Ohio City	1,107	1,017	−90	−8%	NA	NA	NA	NA	NA	NA
Old Brooklyn	3,538	3,912	374	11%	NA	NA	NA	NA	NA	NA
Saint Clair-Superior	1,715	1,288	−427	−25%	NA	NA	NA	NA	NA	NA
Stockyards	1,836	1,839	3	0%	NA	NA	NA	NA	NA	NA
Tremont	1,321	1,061	−260	−20%	NA	NA	NA	NA	NA	NA
Union-Miles Park	3,412	3,029	−383	−11%	NA	NA	NA	NA	NA	NA
University Circle	3,898	4,006	108	3%	NA	NA	NA	NA	NA	NA
West Boulevard	2,857	3,010	153	5%	NA	NA	NA	NA	NA	NA
Woodland Hills	1,416	990	−426	−30%	NA	NA	NA	NA	NA	NA
Total	64,556	61,044	−3,512	−5%	50,100	−10,944	−18%	48,566	−1,534	−3%

Changing 25 to 34 yrs Population in Cleveland, 2000-2023
Decennial and ACS censuses
	2000	2010	Δ	%	2020	Δ	%	2023	Δ	%
Bellaire-Puritas	2,250	1,733	−517	−23%	NA	NA	NA	NA	NA	NA
Broadway-Slavic Village	4,712	2,986	−1,726	−37%	NA	NA	NA	NA	NA	NA
Brooklyn Centre	1,624	1,204	−420	−26%	NA	NA	NA	NA	NA	NA
Buckeye Shaker	2,685	1,665	−1,020	−38%	NA	NA	NA	NA	NA	NA
Central	1,342	1,653	311	23%	NA	NA	NA	NA	NA	NA
Clark Fulton	1,609	1,165	−444	−28%	NA	NA	NA	NA	NA	NA
Collinwood Nottingham	2,343	1,383	−960	−41%	NA	NA	NA	NA	NA	NA
Cudell	1,936	1,372	−564	−29%	NA	NA	NA	NA	NA	NA
Cuyahoga Valley	357	375	18	5%	NA	NA	NA	NA	NA	NA
Detroit-Shoreway	2,159	1,717	−442	−20%	NA	NA	NA	NA	NA	NA
Downtown	1,782	3,109	1,327	74%	NA	NA	NA	NA	NA	NA
Edgewater	1,694	1,300	−394	−23%	NA	NA	NA	NA	NA	NA
Euclid Green	808	602	−206	−25%	NA	NA	NA	NA	NA	NA
Fairfax	863	597	−266	−31%	NA	NA	NA	NA	NA	NA
Glenville	4,635	2,934	−1,701	−37%	NA	NA	NA	NA	NA	NA
Goodrich-Kirtland Park	789	596	−193	−24%	NA	NA	NA	NA	NA	NA
Hopkins	46	109	63	137%	NA	NA	NA	NA	NA	NA
Hough	1,627	1,208	−419	−26%	NA	NA	NA	NA	NA	NA
Jefferson	3,132	2,299	−833	−27%	NA	NA	NA	NA	NA	NA
Kamms Corners	4,340	3,600	−740	−17%	NA	NA	NA	NA	NA	NA
Kinsman	1,267	810	−457	−36%	NA	NA	NA	NA	NA	NA
Lee-Harvard	1,093	827	−266	−24%	NA	NA	NA	NA	NA	NA
Lee-Seville	575	417	−158	−27%	NA	NA	NA	NA	NA	NA
Mount Pleasant	2,989	1,834	−1,155	−39%	NA	NA	NA	NA	NA	NA
North Shore Collinwood	2,871	1,606	−1,265	−44%	NA	NA	NA	NA	NA	NA
Ohio City	1,499	1,681	182	12%	NA	NA	NA	NA	NA	NA
Old Brooklyn	6,266	4,498	−1,768	−28%	NA	NA	NA	NA	NA	NA
Saint Clair-Superior	1,536	758	−778	−51%	NA	NA	NA	NA	NA	NA
Stockyards	1,804	1,392	−412	−23%	NA	NA	NA	NA	NA	NA
Tremont	1,660	1,822	162	10%	NA	NA	NA	NA	NA	NA
Union-Miles Park	3,169	1,909	−1,260	−40%	NA	NA	NA	NA	NA	NA
University Circle	1,463	1,134	−329	−22%	NA	NA	NA	NA	NA	NA
West Boulevard	3,422	2,715	−707	−21%	NA	NA	NA	NA	NA	NA
Woodland Hills	1,280	841	−439	−34%	NA	NA	NA	NA	NA	NA
Total	71,847	53,996	−17,851	−25%	62,334	8,338	15%	63,832	1,498	2%

Changing 35 to 44 yrs Population in Cleveland, 2000-2023
Decennial and ACS censuses
	2000	2010	Δ	%	2020	Δ	%	2023	Δ	%
Bellaire-Puritas	2,459	1,827	−632	−26%	NA	NA	NA	NA	NA	NA
Broadway-Slavic Village	4,897	2,829	−2,068	−42%	NA	NA	NA	NA	NA	NA
Brooklyn Centre	1,670	1,169	−501	−30%	NA	NA	NA	NA	NA	NA
Buckeye Shaker	2,510	1,526	−984	−39%	NA	NA	NA	NA	NA	NA
Central	1,355	957	−398	−29%	NA	NA	NA	NA	NA	NA
Clark Fulton	1,676	1,113	−563	−34%	NA	NA	NA	NA	NA	NA
Collinwood Nottingham	2,489	1,404	−1,085	−44%	NA	NA	NA	NA	NA	NA
Cudell	1,643	1,280	−363	−22%	NA	NA	NA	NA	NA	NA
Cuyahoga Valley	323	173	−150	−46%	NA	NA	NA	NA	NA	NA
Detroit-Shoreway	2,105	1,573	−532	−25%	NA	NA	NA	NA	NA	NA
Downtown	1,182	1,181	−1	−0%	NA	NA	NA	NA	NA	NA
Edgewater	1,115	920	−195	−17%	NA	NA	NA	NA	NA	NA
Euclid Green	1,027	557	−470	−46%	NA	NA	NA	NA	NA	NA
Fairfax	1,137	637	−500	−44%	NA	NA	NA	NA	NA	NA
Glenville	5,670	2,859	−2,811	−50%	NA	NA	NA	NA	NA	NA
Goodrich-Kirtland Park	672	537	−135	−20%	NA	NA	NA	NA	NA	NA
Hopkins	60	78	18	30%	NA	NA	NA	NA	NA	NA
Hough	2,222	1,192	−1,030	−46%	NA	NA	NA	NA	NA	NA
Jefferson	3,362	2,495	−867	−26%	NA	NA	NA	NA	NA	NA
Kamms Corners	4,435	3,584	−851	−19%	NA	NA	NA	NA	NA	NA
Kinsman	1,246	718	−528	−42%	NA	NA	NA	NA	NA	NA
Lee-Harvard	1,557	1,119	−438	−28%	NA	NA	NA	NA	NA	NA
Lee-Seville	785	524	−261	−33%	NA	NA	NA	NA	NA	NA
Mount Pleasant	3,488	1,995	−1,493	−43%	NA	NA	NA	NA	NA	NA
North Shore Collinwood	3,156	2,102	−1,054	−33%	NA	NA	NA	NA	NA	NA
Ohio City	1,408	1,105	−303	−22%	NA	NA	NA	NA	NA	NA
Old Brooklyn	5,845	4,779	−1,066	−18%	NA	NA	NA	NA	NA	NA
Saint Clair-Superior	1,722	816	−906	−53%	NA	NA	NA	NA	NA	NA
Stockyards	1,776	1,334	−442	−25%	NA	NA	NA	NA	NA	NA
Tremont	1,413	1,084	−329	−23%	NA	NA	NA	NA	NA	NA
Union-Miles Park	3,878	2,173	−1,705	−44%	NA	NA	NA	NA	NA	NA
University Circle	651	368	−283	−43%	NA	NA	NA	NA	NA	NA
West Boulevard	3,435	2,637	−798	−23%	NA	NA	NA	NA	NA	NA
Woodland Hills	1,258	745	−513	−41%	NA	NA	NA	NA	NA	NA
Total	73,822	49,555	−24,267	−33%	43,901	−5,654	−11%	44,705	804	2%

Changing 45 to 54 yrs Population in Cleveland, 2000-2023
Decennial and ACS censuses
	2000	2010	Δ	%	2020	Δ	%	2023	Δ	%
Bellaire-Puritas	1,829	2,251	422	23%	NA	NA	NA	NA	NA	NA
Broadway-Slavic Village	3,355	3,430	75	2%	NA	NA	NA	NA	NA	NA
Brooklyn Centre	1,200	1,354	154	13%	NA	NA	NA	NA	NA	NA
Buckeye Shaker	2,097	2,028	−69	−3%	NA	NA	NA	NA	NA	NA
Central	960	1,233	273	28%	NA	NA	NA	NA	NA	NA
Clark Fulton	1,074	1,178	104	10%	NA	NA	NA	NA	NA	NA
Collinwood Nottingham	1,749	1,797	48	3%	NA	NA	NA	NA	NA	NA
Cudell	1,254	1,372	118	9%	NA	NA	NA	NA	NA	NA
Cuyahoga Valley	169	169	0	0%	NA	NA	NA	NA	NA	NA
Detroit-Shoreway	1,610	1,697	87	5%	NA	NA	NA	NA	NA	NA
Downtown	714	1,241	527	74%	NA	NA	NA	NA	NA	NA
Edgewater	739	904	165	22%	NA	NA	NA	NA	NA	NA
Euclid Green	959	796	−163	−17%	NA	NA	NA	NA	NA	NA
Fairfax	984	914	−70	−7%	NA	NA	NA	NA	NA	NA
Glenville	4,442	4,071	−371	−8%	NA	NA	NA	NA	NA	NA
Goodrich-Kirtland Park	589	744	155	26%	NA	NA	NA	NA	NA	NA
Hopkins	41	88	47	115%	NA	NA	NA	NA	NA	NA
Hough	1,689	1,806	117	7%	NA	NA	NA	NA	NA	NA
Jefferson	2,246	2,721	475	21%	NA	NA	NA	NA	NA	NA
Kamms Corners	3,216	3,856	640	20%	NA	NA	NA	NA	NA	NA
Kinsman	1,034	928	−106	−10%	NA	NA	NA	NA	NA	NA
Lee-Harvard	1,495	1,547	52	3%	NA	NA	NA	NA	NA	NA
Lee-Seville	671	657	−14	−2%	NA	NA	NA	NA	NA	NA
Mount Pleasant	2,820	2,586	−234	−8%	NA	NA	NA	NA	NA	NA
North Shore Collinwood	2,297	2,764	467	20%	NA	NA	NA	NA	NA	NA
Ohio City	1,238	1,239	1	0%	NA	NA	NA	NA	NA	NA
Old Brooklyn	4,160	5,245	1,085	26%	NA	NA	NA	NA	NA	NA
Saint Clair-Superior	1,155	1,157	2	0%	NA	NA	NA	NA	NA	NA
Stockyards	1,241	1,414	173	14%	NA	NA	NA	NA	NA	NA
Tremont	1,039	1,213	174	17%	NA	NA	NA	NA	NA	NA
Union-Miles Park	3,044	2,864	−180	−6%	NA	NA	NA	NA	NA	NA
University Circle	608	510	−98	−16%	NA	NA	NA	NA	NA	NA
West Boulevard	2,318	2,744	426	18%	NA	NA	NA	NA	NA	NA
Woodland Hills	915	998	83	9%	NA	NA	NA	NA	NA	NA
Total	55,111	59,726	4,615	8%	42,857	−16,869	−28%	41,195	−1,662	−4%

Changing 55 to 64 yrs Population in Cleveland, 2000-2023
Decennial and ACS censuses
	2000	2010	Δ	%	2020	Δ	%	2023	Δ	%
Bellaire-Puritas	1,211	1,544	333	27%	NA	NA	NA	NA	NA	NA
Broadway-Slavic Village	2,165	2,348	183	8%	NA	NA	NA	NA	NA	NA
Brooklyn Centre	712	916	204	29%	NA	NA	NA	NA	NA	NA
Buckeye Shaker	1,206	1,695	489	41%	NA	NA	NA	NA	NA	NA
Central	622	933	311	50%	NA	NA	NA	NA	NA	NA
Clark Fulton	670	772	102	15%	NA	NA	NA	NA	NA	NA
Collinwood Nottingham	1,107	1,254	147	13%	NA	NA	NA	NA	NA	NA
Cudell	697	931	234	34%	NA	NA	NA	NA	NA	NA
Cuyahoga Valley	55	81	26	47%	NA	NA	NA	NA	NA	NA
Detroit-Shoreway	966	1,226	260	27%	NA	NA	NA	NA	NA	NA
Downtown	354	727	373	105%	NA	NA	NA	NA	NA	NA
Edgewater	419	656	237	57%	NA	NA	NA	NA	NA	NA
Euclid Green	490	715	225	46%	NA	NA	NA	NA	NA	NA
Fairfax	745	804	59	8%	NA	NA	NA	NA	NA	NA
Glenville	2,897	3,175	278	10%	NA	NA	NA	NA	NA	NA
Goodrich-Kirtland Park	388	619	231	60%	NA	NA	NA	NA	NA	NA
Hopkins	48	67	19	40%	NA	NA	NA	NA	NA	NA
Hough	1,107	1,412	305	28%	NA	NA	NA	NA	NA	NA
Jefferson	1,310	1,815	505	39%	NA	NA	NA	NA	NA	NA
Kamms Corners	2,080	2,941	861	41%	NA	NA	NA	NA	NA	NA
Kinsman	769	753	−16	−2%	NA	NA	NA	NA	NA	NA
Lee-Harvard	1,405	1,376	−29	−2%	NA	NA	NA	NA	NA	NA
Lee-Seville	614	553	−61	−10%	NA	NA	NA	NA	NA	NA
Mount Pleasant	1,834	2,106	272	15%	NA	NA	NA	NA	NA	NA
North Shore Collinwood	1,380	2,370	990	72%	NA	NA	NA	NA	NA	NA
Ohio City	673	1,166	493	73%	NA	NA	NA	NA	NA	NA
Old Brooklyn	2,698	3,765	1,067	40%	NA	NA	NA	NA	NA	NA
Saint Clair-Superior	740	729	−11	−1%	NA	NA	NA	NA	NA	NA
Stockyards	888	859	−29	−3%	NA	NA	NA	NA	NA	NA
Tremont	614	803	189	31%	NA	NA	NA	NA	NA	NA
Union-Miles Park	2,506	2,277	−229	−9%	NA	NA	NA	NA	NA	NA
University Circle	500	522	22	4%	NA	NA	NA	NA	NA	NA
West Boulevard	1,400	1,841	441	32%	NA	NA	NA	NA	NA	NA
Woodland Hills	631	725	94	15%	NA	NA	NA	NA	NA	NA
Total	35,987	44,700	8,713	24%	51,614	6,914	15%	49,383	−2,231	−4%

Changing 65+ yr Population in Cleveland, 2000-2023
Decennial and ACS censuses
	2000	2010	Δ	%	2020	Δ	%	2023	Δ	%

Show the code

my_split_gt <- function(x, .rpt_group, .var_name, .var_val, .spanner, .title, .year) {
  x |>
    filter(rpt_group == .rpt_group) |>
    mutate(geo = if_else(!!ensym(.var_name) == .var_val, "MAIN", "Other")) |>
    mutate(.by = geo, pct = value / sum(value)) |>
    as_tibble() |>
    summarize(.by = c(geo, rpt_level), across(c(value, pct), sum)) |>
    pivot_wider(names_from = geo, values_from = c(value, pct)) |>
    arrange(rpt_level) |>
    mutate(
      rpt_level = fct_drop(rpt_level),
      value_Total = value_MAIN + value_Other,
      pct_Total = value_Total / sum(value_Total)
    ) |>
    janitor::adorn_totals() |>
    select(
      rpt_level, ends_with("MAIN"), ends_with("Other"), ends_with("Total")
    ) |>
    gt() |>
    gt::cols_align("left", 1) |>
    gt::fmt_number(columns = c(2, 4, 6), decimals = 0) |>
    gt::fmt_percent(columns = c(3, 5, 7), decimals = 1) |>
    gt::tab_spanner(.spanner, 2:3) |>
    gt::tab_spanner("Other", 4:5) |>
    gt::tab_spanner("Total", 6:7) |>
    gt::cols_label(
      rpt_level = "", starts_with("value") ~ "Est.", starts_with("pct") ~ "%"
    ) |>
    gt::tab_header(
      glue("{.title} Population, {.year}"),
      glue("by {.rpt_group}")
    ) |>
    gt::tab_options(heading.align = "left") 
}

Footnotes

“Opinion: Downtown Cleveland’s strategy to broaden appeal sees success”, Crains Cleveland Business. “Cleveland’s downtown population continues to surge”, Cleveland Fox 19 News.↩︎
Downtown Cleveland Inc. commissioned a report, “Downtown Cleveland Market Study Report” (pdf), by the Urban Partners consulting firm. The report was released in Apr 2023. Figures are from Table 1: 15,330 people in 2010, 18,708 people in 2020 (22% increase).↩︎
See the Downtown neighborhood (statistical processing area, SPA) in the data table.↩︎
“There’s Still No Agreement on How Many Clevelanders Actually Live Downtown”, Cleveland Scene, Sep 17, 2024.↩︎
Cleveland’s population plateaued around 1930 at 900K. The peak was 914K in the 1950 census. Between 1960 and 1980 the population declined by a third. The current population is slightly below the 1900 value. See Visual Cleveland at https://visual.clevelandhistory.org/census/.↩︎
362,670 +/- 62. https://data.census.gov/table/ACSST1Y2023.S0101?q=cleveland,%20oh↩︎
Social Planning Areas (SPAs) were developed in the 1950s to coordinate social services at the neighborhood level. Learn more at the Encyclopedia of Cleveland History. Wikipedia has a nice explanation of how neighborhoods relate to Statistical (or social) Planning Areas.↩︎
From https://data.clevelandohio.gov/, go to the Data Catalog and scroll to Census 2020 Analysis.↩︎
There is a map on page 3 of their report.↩︎